[1]} claim that human annotators consistently rely on contextual information to make judgments regarding sarcastic intent. Accordingly, recent studies attempt to leverage various forms of contextual information mostly external to the utterance, toward more effective sarcasm identification. Intuitively, in the case of Amazon product reviews, knowing the type of books an individual typically likes might inform our judgment: someone who mostly reads and reviews Dostoevsky is statistically being ironic if they write a laudatory review of Twilight. Evidently, many people genuinely enjoy reading Twilight, and so if the review is written subtly, it will likely be difficult to discern the author's intent without this preferential background. Therefore, [2]} report that including features independent of the text leads to ameliorating the performance of sarcasm models. To this end, studies take three forms of context as feature: 1) author context [3]}, [4]}, 2) conversational context [5]}, and 3) topical context [6]}. Another popular line of research utilizes various user embedding techniques that encode users' stylometric and personality features to improve their sarcasm detection models [3]}. Their model, CASCADE, utilizes user embeddings that encode stylometric and personality features of the users. When used along with content-based feature extractors such as Convolutional Neural Networks (CNNs), a significant boost in the classification performance on a large Reddit corpus is achieved. Similarly to how a user controls the degree of sarcasm in a comment, they extrapolate that the ensuing discourse of comments belonging to a particular discussion forum contains contextual information relevant to the sarcasm identification. They embed topical information that selectively incurs bias towards the degree of sarcasm present in the comments of a discussion. For example, comments on political leaders or sports matches are generally more prone to sarcasm than natural disasters. Contextual information extracted from the discourse of a discussion can also provide background knowledge or cues about the discussion topic. To extract the discourse features, they take a similar approach of document modeling performed for stylometric features.
